Retrieval-augmented generation is moving from experimental to production for many enterprise teams. After building RAG systems for clients in legal, finance, and healthcare, we have learned that the architecture decisions you make early have an outsized impact on accuracy and cost.
Chunking is the foundation
How you split documents determines what the model can retrieve. Fixed-size chunks are simple but break context; structure-aware chunking that respects headings and sections consistently produces better answers.
Choose your embedding model deliberately
The default embedding model is rarely the right one. Domain, language, and latency budget all matter. Evaluate two or three candidates against your own data before committing.
Hybrid retrieval and reranking
Dense vector search alone misses exact-match terms; sparse keyword search alone misses meaning. Combining both, then reranking the results, is the single biggest accuracy lever we have found.
Measure everything
Without an evaluation set, you are guessing. Build one early so every change is a measured improvement, not a hopeful one.
Planning a RAG or AI build? See our AI development services, the industries we serve, or hire dedicated AI engineers.
